Kalman filtering based on
the maximum correntropy criterion
in the presence of non-Gaussian noise
Reza Izanloo, Seyed Abolfazl Fakoorian, Hadi Sadoghi, and Dan Simon
This paper deals with state estimation in the presence of non-Gaussian noise. Since the Kalman filter uses only second-order signal information, it is not optimal in non-Gaussian noise environments. The maximum correntropy criterion (MCC) is a new approach to measure the similarity of two random variables. MCC uses information from the higher-order statistics of the signals. The correntropy filter (C-Filter) uses the properties of the MCC for state estimation. This paper first improves the performance of the C-filter by modifying its derivation to obtain a modified correntropy filter (MC-Filter). This paper then uses the properties of MCC and weighted least squares (WLS) to propose an MCC filter in Kalman filter form which we call the MCC-KF. Simulation results show the superiority of the MCC-KF compared with the C-Filter, the MC-Filter, the unscented Kalman filter, the ensemble Kalman filter, and the Gaussian sum filter, in the presence of two different types of non-Gaussian disturbances (shot noise and Gaussian mixture noise).
R. Izanloo, S. Fakoorian, H. Sadoghi, and D. Simon, "Kalman filtering based on the maximum correntropy criterion in the presence of non-Gaussian noise," 50th Annual Conference on Information Systems and Sciences, March 2016 - pdf, 292 KB
Last Revised: February 24, 2016